3-D knowledge structures for customer preference transition

This paper proposes a method to extract and manage customer preference information used to create My-page, which is a customer service of Internet Service Providers. To provide useful information for customers on My-page, it is essential to accurately grasp the transitions of customer preferences as well as market trends. Customer preference information has conventionally been managed with two-dimensional vectors with customer and preference category axes. In this paper, we propose a method that manages customer preference information with three-dimensional vectors with customer, preference category, and time axes, which enables us to accurately grasp the transitions of customer preferences and market trends. The information volume in three-dimensional vectors is enormous compared with that in two-dimensional vectors. The proposed method addresses this problem by storing only positions and values of the points where information changes over time. This reduces the storage for two-dimensional vectors information per unit time to less than 5% of the original volume. Some kind of knowledge dictionary is required to extract customer preference information. Our proposed method dynamically updates the knowledge dictionary to accomplish a distributed system that performs information extraction at each access point. This enables us to reduce 60% of the CPU usage on the marketing server that is used for managing customer preference information.